from abc import ABC, abstractmethod

# Define an abstract base class using ABC
class Shape(ABC):
@abstractmethod
def area(self):
pass

@abstractmethod
def perimeter(self):
pass

# Concrete subclass Circle inheriting from Shape
class Circle(Shape):
def init(self, radius):
self.radius = radius

def area(self):
return 3.14 * self.radius * self.radius

def perimeter(self):
return 2 * 3.14 * self.radius

# Concrete subclass Rectangle inheriting from Shape
class Rectangle(Shape):
def init(self, width, height):
self.width = width
self.height = height

def area(self):
return self.width * self.height

def perimeter(self):
return 2 * (self.width + self.height)

# Attempting to instantiate Shape directly will raise TypeError
try:
s = Shape()
except TypeError as e:
print(f"TypeError: {e}")

# Instantiate Circle and Rectangle objects
circle = Circle(5)
rectangle = Rectangle(4, 6)

# Calculate and print area and perimeter of Circle and Rectangle
print(f"Circle - Area: {circle.area()}, Perimeter: {circle.perimeter()}")
print(f"Rectangle - Area: {rectangle.area()}, Perimeter: {rectangle.perimeter()}")

class Person:
def init(self, name, age):
self.name = name
self.age = age

# Create an instance of the Person class
person = Person('Alice', 30)

# Using getattr to dynamically retrieve attributes
name = getattr(person, 'name')
age = getattr(person, 'age')
city = getattr(person, 'city', 'Unknown') # Providing a default value if attribute doesn't exist

print(f"Name: {name}")
print(f"Age: {age}")
print(f"City: {city}")

import sys

# Print all command-line arguments
print("All arguments:", sys.argv)

# Print the script name
print("Script name:", sys.argv[0])

# Print the command-line arguments excluding the script name
for i in range(1, len(sys.argv)):
print(f"Argument {i}:", sys.argv[i])

If this script is executed with command-line arguments like:

python script.py arg1 arg2 arg3

The output will be:

All arguments: ['script.py', 'arg1', 'arg2', 'arg3']
Script name: script.py
Argument 1: arg1
Argument 2: arg2
Argument 3: arg3

class Point:
def init(self, x, y):
self.x = x
self.y = y

def str(self):
return f"Point({self.x}, {self.y})"

# Creating an instance of the Point class
p = Point(3, 4)

# Printing the instance as a string
print(str(p)) # Output: Point(3, 4)

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dataset
data = load_iris()
X = data.data
y = data.target

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create a logistic regression model
# Hyperparameters: C (regularization strength) and max_iter (number of iterations)
model = LogisticRegression(C=1.0, max_iter=100)

# Fit the model to the training data
model.fit(X_train, y_train)

# Predict on the test data
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

# Display parameters learned by the model
print(f"Model Coefficients: {model.coef_}")
print(f"Model Intercept: {model.intercept_}")